Data Mining Xuequn Shang NorthWestern Polytechnical University

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Data Mining Xuequn Shang NorthWestern Polytechnical University September 2006
About the Course ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mini Survey ,[object Object],[object Object],[object Object]
Textbook and Reference ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Course Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Course Objective ,[object Object],[object Object]
Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object]
About the Project ,[object Object],[object Object],[object Object]
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Course Schedule(1) Session 6 Oct- 13 classification Session 5 Oct- 10 Sequential Pattern Mining Session 4 Sep- 29 Session 3 Sep- 26 Association rule mining Session 2 7:00 pm-9:00 pm Sep- 22 Welcome and introduction Session 1 7:00 pm-9:00 pm Sep- 19 Topic Session Time Date
Course Schedule(2) Seminar Session 12 Nov- 3 Session 11 Oct- 31 Advance topic Session 10 Oct- 27 Session 9 Oct- 24 Data preprocessing Session 8 Oct- 20 Data Clustering Session 7 Oct- 17 Topic Session Time Date
Course Schedule(3) Session 8 Nov- 10 examination Session 7 Nov- 7 Topic Session Time Date
Useful Information ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Additional Hits ,[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Data Mining? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Data Mining? ,[object Object],[object Object],[object Object],[object Object],PHOTO: HULTON-DEUTSCH COLL PHOTO: LUCINDA DOUGLAS-MENZIES
What Is Data Mining? ,[object Object],[object Object],[object Object]
Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ex. 1: Market Analysis and Management ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ex. 2: Corporate Analysis & Risk Management ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ex. 3: Fraud Detection & Mining Unusual Patterns ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The KDD Process ,[object Object],Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],KDD Process Steps
Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization
Classification Schemes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What Kind of Data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Relational Databases
[object Object],Data Warehouses Data Warehouse Clean Transform Integrate Load Query and analysis tools Client Client
[object Object],Data Cube A B 29 30 31 32 1 2 3 4 5 9 13 14 15 16 64 63 62 61 48 47 46 45 a1 a0 c3 c2 c1 c 0 b3 b2 b1 b0 a2 a3 C B 44 28 56 40 24 52 36 20 60
Transactional Databases What kind of product combinations that customers like to buy together? … … Beer, cook, fish, potato, orange, apple T200 Milk, bread, beer, diaper T100 Itemset TID
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Spatial Databases
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Time Series Data
[object Object],[object Object],[object Object],[object Object],[object Object],Semi-Structure Data
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Biological Data
[object Object],[object Object],[object Object],[object Object],[object Object],What Can Be Discovered?
What Kinds of Patterns? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Are All the “Discovered” Patterns Interesting? ,[object Object],[object Object],[object Object],[object Object]
What makes a pattern interesting? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Find All Interesting Patterns? ,[object Object],[object Object],[object Object],[object Object]
Find Only Interesting Patterns? ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],Research Issues in Data Mining
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Effectiveness
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Efficiency
[object Object],[object Object],[object Object],[object Object],Applications
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Foundation for Data Mining
Major Issues in Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A Brief History of Data Mining Society ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Assignment (Ⅰ) ,[object Object],[object Object]
Assignment (Ⅱ) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Complement (Ⅰ) ,[object Object],[object Object],[object Object]
Complement (Ⅱ) ,[object Object],[object Object],[object Object],[object Object],Data mining involves an integration of techniques from multiple disciplines
Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Knowledge-Base Database Data Warehouse World-Wide Web Other Info Repositories
Thank you !
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Data Mining Xuequn Shang NorthWestern Polytechnical University

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Data Mining Xuequn Shang NorthWestern Polytechnical University

  • 1. Data Mining Xuequn Shang NorthWestern Polytechnical University September 2006
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10. Course Schedule(1) Session 6 Oct- 13 classification Session 5 Oct- 10 Sequential Pattern Mining Session 4 Sep- 29 Session 3 Sep- 26 Association rule mining Session 2 7:00 pm-9:00 pm Sep- 22 Welcome and introduction Session 1 7:00 pm-9:00 pm Sep- 19 Topic Session Time Date
  • 11. Course Schedule(2) Seminar Session 12 Nov- 3 Session 11 Oct- 31 Advance topic Session 10 Oct- 27 Session 9 Oct- 24 Data preprocessing Session 8 Oct- 20 Data Clustering Session 7 Oct- 17 Topic Session Time Date
  • 12. Course Schedule(3) Session 8 Nov- 10 examination Session 7 Nov- 7 Topic Session Time Date
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25. Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31. Transactional Databases What kind of product combinations that customers like to buy together? … … Beer, cook, fish, potato, orange, apple T200 Milk, bread, beer, diaper T100 Itemset TID
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53.
  • 54. Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Knowledge-Base Database Data Warehouse World-Wide Web Other Info Repositories